This implies that style transfer must become more closely tied to mainstream computer vision tasks such as recognition, localization, and depth prediction. Not only will this benefit style transfer, developing systems which can parse the semantics and geometry of artwork can lead to models which better mimic humans' robust ability to understand scenes. Systems trained using large datasets of photographs improve every year on benchmarks where the test data matches the training distribution, but performance drops catastrophically when the distribution of test data shifts [1]}. This is in stark contrast to humans, who can typically understand semantics, even in artistic styles they have never seen before.
When we go to the art museum, it is common to encounter works by unfamiliar artists who produce images in a visually distinctive style. Yet we are easily, often automatically, able to recognize the semantic contents of these images. Representational art, unlike natural imagery, is a product of the artist's internal content representations. Not only that, it is specially designed to be recognizable to other humans. These properties make representational art an invaluable window into the invariances of the human visual system. I hope that future work, my own and others', will continue to explore and leverage the connection between our capabilities in recognition and creativity.
